ANIL KUMAR VEERAPANENI¹, CHANDRASEKAR KUPPAN^2,*, MURTHY CHAVALI^3,*

Journal of Polymer Materials, Vol.37, No.3-4, pp. 121-130, 2020, DOI:10.32381/JPM.2020.37.3-4.1

Abstract The mechanical properties of hybrid nanocomposites made of epoxy/glass fibre dispersed with nano-α-Al₂ O₃ powder at different weight percentages were studied.The effect of nano-α- Al₂O₃ size and wt% on mechanical properties like tensile, flexural, interlaminar shear stress and hardness enhanced because of their higher surface area and interfacial polymer-metal interaction. The nanoparticle embedded laminates have shown improvement in flexural strength,and hardness when compared to laminate without nano-α-Al₂ O₃. The properties varied with the loading and size of the nanoparticles. The tensile strength was highest for 0.5 wt% of 200nm nano-α-Al₂O₃, which is 167.80 N/m₂.The highest flexural strength was observed for… More >

HADIMANI SHIVAKUMAR¹, GURUMURTHY G. D.¹, BOMMEGOWDA K. B.², S. PARAMESHWARA³

Journal of Polymer Materials, Vol.40, No.1-2, pp. 93-103, 2023, DOI:10.32381/JPM.2023.40.1-2.8

Abstract Polymer composites are increasing in demand in energy storage applications including in the electronic as well as electrical industries due to the ease of processing of these materials with associated advantages like light weight, corrosion resistance, and high mechanical strength. In this investigation, efforts are made to enhance the charging and discharging properties of epoxy/carbon fabric composite by the addition of graphene nanoplatelets (GNPs) into the epoxy/ carbon matrix. The performance of the composites with graphene platelets of 0.5 to 5 wt. % in epoxy were characterized and 1wt.% percolation threshold was observed poor performance in gravimetric charge and discharge… More >

Hong Huang¹, Xingxing Zhang^1,*, Ye Lu¹, Ze Li¹, Shaohua Zhou²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity,… More >

Duy Quang Tran¹, Huy Q. Tran^2,*, Minh Van Nguyen³

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3585-3602, 2024, DOI:10.32604/cmc.2024.047760

Abstract With the advancement of artificial intelligence, traffic forecasting is gaining more and more interest in optimizing route planning and enhancing service quality. Traffic volume is an influential parameter for planning and operating traffic structures. This study proposed an improved ensemble-based deep learning method to solve traffic volume prediction problems. A set of optimal hyperparameters is also applied for the suggested approach to improve the performance of the learning process. The fusion of these methodologies aims to harness ensemble empirical mode decomposition’s capacity to discern complex traffic patterns and long short-term memory’s proficiency in learning temporal relationships. Firstly, a dataset for… More >

Muhammad Armghan Latif¹, Zohaib Mushtaq², Saad Arif³, Sara Rehman⁴, Muhammad Farrukh Qureshi⁵, Nagwan Abdel Samee⁶, Maali Alabdulhafith^6,*, Yeong Hyeon Gu⁷, Mohammed A. Al-masni⁷

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4225-4241, 2024, DOI:10.32604/cmc.2024.047621

Abstract Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland. Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care. This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques. Sequential forward feature selection, sequential backward feature elimination, and bidirectional feature elimination are investigated in this study. In ensemble learning, random forest, adaptive boosting, and bagging classifiers are employed. The effectiveness of these techniques is evaluated using… More >

Qazi Mazhar ul Haq¹, Fahim Arif^2,3, Khursheed Aurangzeb⁴, Noor ul Ain³, Javed Ali Khan⁵, Saddaf Rubab⁶, Muhammad Shahid Anwar^7,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4379-4397, 2024, DOI:10.32604/cmc.2024.047172

Abstract Software project outcomes heavily depend on natural language requirements, often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements. Researchers are exploring machine learning to predict software bugs, but a more precise and general approach is needed. Accurate bug prediction is crucial for software evolution and user training, prompting an investigation into deep and ensemble learning methods. However, these studies are not generalized and efficient when extended to other datasets. Therefore, this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems. The methods involved feature selection, which is used to… More >

Shuting Ge^1,2, Jin Ren^2,3,*, Yihua Shi⁴, Yujun Zhang¹, Shunzhi Yang², Jinfeng Yang²

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3215-3245, 2024, DOI:10.32604/cmc.2023.046746

Abstract In air traffic control communications (ATCC), misunderstandings between pilots and controllers could result in fatal aviation accidents. Fortunately, advanced automatic speech recognition technology has emerged as a promising means of preventing miscommunications and enhancing aviation safety. However, most existing speech recognition methods merely incorporate external language models on the decoder side, leading to insufficient semantic alignment between speech and text modalities during the encoding phase. Furthermore, it is challenging to model acoustic context dependencies over long distances due to the longer speech sequences than text, especially for the extended ATCC data. To address these issues, we propose a speech-text multimodal… More >

Shoukun Xu¹, Lujun Zhang¹, Guangqi Jiang¹, Yining Hua², Yi Liu^1,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3021-3039, 2024, DOI:10.32604/cmc.2023.045853

Abstract This paper focuses on the task of few-shot 3D point cloud semantic segmentation. Despite some progress, this task still encounters many issues due to the insufficient samples given, e.g., incomplete object segmentation and inaccurate semantic discrimination. To tackle these issues, we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity, which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks (CapsNets) in the embedding network. Concretely, the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations, which capture the relationships… More >

M. L. Maruthi Prasad^*, R. Santhosh

Intelligent Automation & Soft Computing, Vol.39, No.1, pp. 1-13, 2024, DOI:10.32604/iasc.2023.036628

Abstract This research concentrates to model an efficient thyroid prediction approach, which is considered a baseline for significant problems faced by the women community. The major research problem is the lack of automated model to attain earlier prediction. Some existing model fails to give better prediction accuracy. Here, a novel clinical decision support system is framed to make the proper decision during a time of complexity. Multiple stages are followed in the proposed framework, which plays a substantial role in thyroid prediction. These steps include i) data acquisition, ii) outlier prediction, and iii) multi-stage weight-based ensemble learning process (MS-WEL). The weighted… More >

Seyfolah Saedodin, M.S. Motaghedi Barforoush, Mohsen Torabi^*

Frontiers in Heat and Mass Transfer, Vol.2, No.4, pp. 1-4, 2011, DOI:10.5098/hmt.v2.4.4001

Abstract In this paper, a simplifying approach for calculating the radiant energy is achieved using the concept of net radiation heat transfer and provides an easy way for solving a variety of situations. This method has been applied to calculate the net radiation heat transfer between two long concentric semi-cylinders. Then this method used to calculate reduction heat transfer when radiation shields with temperature-dependent emissivity applied between these objects. Moreover, using this method the percentage reduction in heat transfer between two surfaces was calculated. The findings reveal that, one radiation shield with lower emissivity can reduce the net heat transfer even… More >